Step size, also known as learning rate, is a crucial hyperparameter in optimization algorithms that determines the size of the steps taken towards a minimum of a loss function. Choosing an appropriate step size is essential for ensuring convergence and stability of the learning process, as too large a step size can lead to overshooting, while too small a step size can result in slow convergence.